Smart Home Device Detection Algorithm Based on FSA-YOLOv5
This work addresses smart home device detection for human-computer interaction, but it is incremental as it builds on YOLOv5 with modifications.
The paper tackles the problem of detecting smart home devices in indoor environments, which is challenging due to ambient light and background noise, and presents FSA-YOLOv5, a model that outperforms other methods on the SUSSD dataset.
Smart home device detection is a critical aspect of human-computer interaction. However, detecting targets in indoor environments can be challenging due to interference from ambient light and background noise. In this paper, we present a new model called FSA-YOLOv5, which addresses the limitations of traditional convolutional neural networks by introducing the Transformer to learn long-range dependencies. Additionally, we propose a new attention module, the full-separation attention module, which integrates spatial and channel dimensional information to learn contextual information. To improve tiny device detection, we include a prediction head for the indoor smart home device detection task. We also release the Southeast University Indoor Smart Speaker Dataset (SUSSD) to supplement existing data samples. Through a series of experiments on SUSSD, we demonstrate that our method outperforms other methods, highlighting the effectiveness of FSA-YOLOv5.